A Neural Net Based Prediction of Sound Pressure Level for the Design of the Aerofoil

Abstract

Aerofoil self-noise can affect the performance of the overall system. One of the main goals of aircraft design is to create an aerofoil with minimum weight, cost, and self-noise, satisfying all design requirements from the physical and the functional requirements. Aerofoil self-noise refers to the noise produced by the interaction between an aerofoil and its boundary layer. This paper describes how the prediction of the self-noise of an aerofoil at the early stage of the design phase can help select the best design of the aerofoil, which in turn reduces the lead time as the design process becomes more robust with respect to cost effectiveness. In the present work, the prediction of the self-noise of the aerofoil is addressed using Neural Networks (NN). Different architectures are used along with various proportions of training and testing to select the best architecture and best training-testing ratio. The results from NN is compared with linear, quadratic, and cubic polynomial regression. Thereafter, Principal Component Analysis (PCA) is integrated with NN for further improvement of prediction results. Our experimental results indicate that neural networks outperform regression. Moreover, PCA integrated with NN outperforms even the best neural network result.